Introduction

In this exercise we want to analyse the impact of COVID-19 on vessel densities in the Belgian Part of the North Sea and the Western Scheldt. There are different ways to calculate vessel densities, therefore we first start with some background information on two different types of vessel densities and where this data can be found. The data and code of this analysis if freely available, see the ‘Data availability’ and ‘Code availability’ sections at the end of this document.

Vessel densities

In this small exercise we want to analyse vessel route densities in the Belgian part of the North Sea. The data for this exercise comes from EMODnet Human activities. EMODnet Human Activities has two types of vessel density data, one created by the Human Activities portal themselves, giving the vessel hours per square km per month by ship type. See here.

And one created by the European Maritime Safety Agency (EMSA), giving the number of routes per square km per month) by ship type. The advantage of the latter is that this provides recent information. For example, writing now 28th of April, the monthly aggregated data is already available for March 2020. For details, see here

For this exercise, we are only interested in Belgian waters, so we’ll use bounding box of the Belgian Exclusive Economic ZOne:

## Reading layer `OGRGeoJSON' from data source `http://geo.vliz.be/geoserver/MarineRegions/wfs?service=WFS&version=2.0.0&request=GetFeature&typeNames=eez&cql_filter=mrgid=%273293%27&outputFormat=application/json' using driver `GeoJSON'
## Simple feature collection with 1 feature and 32 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 2.238333 ymin: 51.08931 xmax: 3.370403 ymax: 51.87611
## geographic CRS: WGS 84

For the Scheldt Estuary:

## Reading layer `OGRGeoJSON' from data source `http://geo.vliz.be/geoserver/wfs?request=getfeature&service=wfs&version=1.1.0&typename=MarineRegions:seavox_v16&outputformat=json&filter=%3CPropertyIsEqualTo%3E%3CPropertyName%3Esub_region%3C%2FPropertyName%3E%3CLiteral%3EWESTERN+SCHELDT%3C%2FLiteral%3E%3C%2FPropertyIsEqualTo%3E' using driver `GeoJSON'
## Simple feature collection with 1 feature and 23 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 3.546714 ymin: 51.32974 xmax: 4.252759 ymax: 51.46375
## geographic CRS: WGS 84

Now we extract the Vessel density data, for example here for the Fhishing map of January 2019:

Belgian EEZ:

We extract all data from all the cells, and calculate the average for the whole BCP.

This is an example of the data in ‘wide’ format

datatable(df.sr,
          options = list(pageLength = 16),
          rownames = FALSE) %>% formatRound(-1, 2)

And this is the table in the ‘long’ format:

datatable(df.sr.long,
          options = list(pageLength = 16),
          rownames = FALSE) %>% formatRound(3, 2)

A plot of the result:

Scheldt estuary:

datatable(df.Scheldt,
          options = list(pageLength = 16),
          rownames = FALSE) %>% formatRound(-1, 2)

A plot of the result:

2020 vs 2019

We create a mean value for

  • February - April 2019
  • February - April 2020

And plot both periods rasters (the map is the Fishing type example)

BCP maps 2019 vs 2020

Scheldt maps 2019 vs 2020

Anomaly maps

Comparing the period February-April 2020 vs 2019: (the different layers are the different boat types)

Comparing the month-by-month differences between 2020 vs 2019: (the map is the Fishing type example)

Data availability

  • Vessel densities The data from this exercise is freely available at the EMODnet Human activities portal. EMODnet stands for the European Marine Observation and Data Network, EMODnet is a network of organisations that are collecting, standardizing and making European marine data and data products freely available, supported by EU’s integrated marine policy. The vessel densities used in this exercise is provided by the European Maritime Safety Agency (EMSA) to EMODnet human activities and available here.

  • Maritime boundaries The maritime boundaries used in this exercise are from MarineRegions.org. MarineRegions.org maintains a standard, relational list of geographic names coupled with information and maps of the geographic location of these features. This improves access and clarity of the different geographic, marine names and allows an improved linking of these locations to databases. The more information about the polygons used in this exercise:

Code availability

All the code needed to run this analysis is available here.

This code makes use of following R packages: - raster: for raster data - sf: for spatial data - mapview: for interactive maps - ggplot2: for plots - data.table: for manipulation dataframes/tables - mregions: for standardize marine regions from http://www.marineregions.org